System and Method for Locating, Identifying and Counting Items

ABSTRACT

A system for building a product library without requiring a planogram includes an image capture unit operated to provide images of items. Taking data from a shelf label detector and depth map creation unit, a processing module can be used to compare detected shelf labels to a depth map, define a product bounding box, and associate the bounding box with an image provided by the image capture unit to build image descriptors. The system can include one or more autonomous robots for supporting and moving the image capture unit and other components.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

The present disclosure is part of a non-provisional patent applicationclaiming the priority benefit of U.S. Patent Application No. 62/314,785,“System and Method for Locating, Identifying and Counting Products onShelves”, filed Mar. 29, 2016; and U.S. Patent Application No.62/427,509, “System and Method for Locating, Identifying and CountingItems”, filed Nov. 29, 2016.

TECHNICAL FIELD

The present disclosure relates generally to a multiple camera sensorsuite capable of accurately monitoring retail or warehouse productinventory without needing an initial planogram. In certain embodiments,the multiple camera sensor suite can be mounted on an autonomous robotand include onboard processing to provide near real time producttracking.

BACKGROUND

Retail stores or warehouses can have thousands of distinct products thatare often sold, removed, added, or repositioned. Even with frequentrestocking schedules, products assumed to be in stock may be out ofstock, decreasing both sales and customer satisfaction. Point of salesdata can be used to roughly estimate product availability, but does nothelp with identifying misplaced, stolen, or damaged products, all ofwhich can reduce product availability. However, manually monitoringproduct inventory and tracking product position is expensive and timeconsuming.

One solution for tracking product inventory relies on planograms (listsor diagrams that show how and where specific products should be placedon shelves or displays) in combination with machine vision technology.Given a planogram, machine vision can be used to assist in shelf spacecompliance. For example, large numbers of fixed position cameras can beused throughout a store to monitor aisles, with large gaps in shelfspace being checkable against the planogram or shelf labels, and flaggedas “out of stock” if necessary. Alternatively, a smaller number ofmovable cameras can be used to scan a store aisle. Even with suchsystems, human intervention is generally required to build an initialplanogram that includes detailed information relative to a bounding boxthat can include product identification, placement, and count.Substantial human intervention can also be required to update theplanogram, as well as search for misplaced product inventory.

SUMMARY

A low cost, accurate, and scalable camera system for product or otherinventory monitoring can include a movable base. Multiple camerassupported by the movable base are directable toward shelves or othersystems for holding products or inventory. A processing module isconnected to the multiple cameras and able to construct from the cameraderived images an updateable map of product or inventory position.

In some embodiments, the described camera system for inventorymonitoring can be used for detecting shelf labels; optionally comparingshelf labels to a depth map; defining a product bounding box;associating the bounding box to a shelf label to build a training dataset; and using the training data set to train a product classifier.

In other embodiments, a system for building a product library caninclude an image capture unit operated to provide images of items. Thesystem also includes a shelf label detector (which can be a highresolution zoomable camera) and optionally depth map creation unit(which can be provided by laser scanning, time-of-flight range sensing,or stereo imaging), a processing module to optionally compare detectedshelf labels to a depth map, define a product bounding box, andassociate the bounding box with a shelf label to build a training dataset or learn image descriptors. Both the image capture unit andprocessing module can be mounted on an autonomous robot.

Because it represents reality on the shelf, an inventory map such asdisclosed herein can be known as a “realogram” to distinguish fromconventional “planograms” that take the form of 3D models, cartoons,diagrams or lists that show how and where specific retail products andsignage should be placed on shelves or displays. Realograms can belocally stored with a data storage module connected to the processingmodule. A communication module can be connected to the processing moduleto transfer realogram data to remote locations, including store serversor other supported camera systems, and additionally receive inventoryinformation including planograms to aid in realogram construction. Inaddition to realogram mapping, this system can be used to detect out ofstock products, estimate depleted products, estimate amount of productsincluding in stacked piles, estimate products heights, lengths andwidths, build 3D models of products, determine products' positions andorientations, determine whether one or more products are in disorganizedon-shelf presentation that requires corrective action such as facing orzoning operations, estimate freshness of products such as produce,estimate quality of products including packaging integrity, locateproducts, including at home locations, secondary locations, top stock,bottom stock, and in the backroom, detect a misplaced product event(also known as a plug), identify misplaced products, estimate or countthe number of product facings, compare the number of product facings tothe planogram, locate labels, determine sequence of labels, detect labeltype, read label content, including product name, barcode, UPC code andpricing, detect missing labels, compare label locations to theplanogram, compare product locations to the planogram, determine theexpiration date of products, determine freshness of products includingproduce, measure shelf height, shelf depth, shelf width and sectionwidth, recognize signage, detect promotional material, includingdisplays, signage, and features and measure their bring up and downtimes, detect and recognize seasonal and promotional products anddisplays such as product islands and features, capture images ofindividual products and groups of products and fixtures such as entireaisles, shelf sections, specific products on an aisle, and productdisplays and islands, capture 360-deg and spherical views of theenvironment to be visualized in a virtual tour application allowing forvirtual walk throughs, capture 3D images of the environment to be viewedin augmented or virtual reality, capture environmental conditionsincluding ambient light levels, capture information about theenvironment including measuring space compliance with disability andsafety standards and determining if light bulbs are off, provide areal-time video feed of the space to remote monitors, provide on-demandimages and videos of specific locations, including in live or scheduledsettings, and build a library of product images.

In one embodiment, the movable base can be a manually pushed or guidablecart. Alternatively, the movable base can be a tele-operated robot, orin preferred embodiments, an autonomous robot capable of guiding itselfthrough a store or warehouse. Depending on size of the store orwarehouse, multiple autonomous robots can be used. Aisles can beregularly inspected to identify out of stocks or create realograms, withaisles having high product movement being inspected more often.

In another embodiment, an inventory monitoring method includes the stepsof allowing an autonomous robot to move along an aisle that is linedwith shelves capable of holding inventory or products, with theautonomous robot acting as a movable base for multiple cameras. Multiplecameras are directed toward inventory on the shelf lined aisle, withdata derived at least in part from these cameras being used to constructa realogram of inventory or a panoramic image using a processing modulecontained in the autonomous robot. Realogram data or panoramic imagescreated by the processing module can be transferred to remote locationsusing a communication module, and inventory information received via thecommunication module can be used to aid in realogram construction.

In yet another embodiment, an inventory monitoring method, includes thesteps of allowing an autonomous robot to move along a shelf lined aisleholding inventory, with the autonomous robot acting as a movable basefor multiple cameras. The autonomous robot can maintain a substantiallyconstant or tightly controlled distance from the shelf lined aisleholding inventory while moving in a forward or reverse direction. Usingthe multiple cameras directed toward inventory on the shelf lined aisle,at least part of a realogram of inventory positioned along a shelf linedaisle holding inventory can be constructed. Typically, the realogram iscreated and updated with a locally sited data storage and a processingmodule contained in the autonomous robot. To ensure complete or nearcomplete camera coverage of shelf lined aisles, the autonomous robot canpause, reverse, or mark for further multiple camera inspection if datacapture for a portion of the shelf lined aisle is incomplete.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a camera system mounted on a movable baseto track product changes in aisle shelves or other suitable targets;

FIG. 2 is a cartoon illustrating two autonomous robots inspectingopposite shelves in an aisle;

FIG. 3 is an illustration of various systems and electronic modulesconnected to inventory cameras;

FIG. 4 is an illustration of steps in one embodiment of operation;

FIG. 5A and B are respectively examples in side view and cross sectionof an autonomous robot capable of acting as a mobile base for a camerasystem; and

FIG. 6 is a flowchart 600 illustrating in more detail how a productspace for a realogram can be created without requiring an initialplanogram; and

FIG. 7 is a flow chart 700 illustrating additional process details formanaging aspects of the disclosed system.

DETAILED DESCRIPTION

FIG. 1 is an illustration of an inventory monitoring camera system 100mounted on a movable base 110 (with drive wheels 114) to track productchanges in aisle shelves or other targets 102 without requiring aninitial planogram.

The movable base 110 can be an autonomous robot having a navigation andobject sensing suite 130 that is capable of independently navigating andmoving throughout a building. The autonomous robot has multiple cameras140 attached to movable base 110 by a vertically extending camerasupport 140. Lights 150 are positioned to direct light toward target102. The object sensing suite includes forward (133), side (134 and135), top (132) and/or rear (not shown) image and depth sensors to aidin object detection, localization, and navigation. Additional sensorssuch as laser ranging units 136 and 138 (and respective laser scanningbeams 137 and 139) also form a part of the sensor suite that is usefulfor accurate distance determination. In certain embodiments, imagesensors can be depth sensors that infer depth from stereo images,project an infrared mesh overlay that allows rough determination ofobject distance in an image, or that infer depth from the time of flightof light reflecting off the target. In other embodiments, simple camerasand various image processing algorithms for identifying object positionand location can be used. For selected applications, ultrasonic sensors,radar systems, magnetometers or the like can be used to aid innavigation. In still other embodiments, sensors capable of detectingelectromagnetic, light, or other location beacons can be useful forprecise positioning of the autonomous robot.

As seen in FIG. 1, various representative camera types useful forconstructing an updatable realogram are shown. As previously noted, arealogram can use camera derived images to produce an updateable map ofproduct or inventory position. Typically, one or more shelf units (e.g.target 102) would be imaged by a diverse set of camera types, includingdownwardly (142 and 144) or upwardly (143 and 148) fixed focal lengthcameras that cover a defined field less than the whole of a target shelfunit; a variable focus camera that adapts its focus to the distance fromthe imaged target; a wide field camera 145 to provide greaterphotographic coverage than the fixed focal length cameras; and a narrowfield, zoomable telephoto 146 to capture bar codes, productidentification numbers, and shelf labels. Alternatively, a highresolution, tilt controllable, height adjustable camera can be used toidentify shelf labels. These camera 140 derived images can be stitchedtogether, with products in the images identified, and positiondetermined.

To simplify image processing and provide accurate results, the multiplecameras are typically positioned a set distance from the shelves duringthe inspection process. The shelves can be illuminated with LED or otherdirectable lights 150 positioned on or near the cameras. The multiplecameras can be linearly mounted in vertical, horizontal, or othersuitable orientation on a camera support. In some embodiments, to reducecosts, multiple cameras are fixedly mounted on a camera support. Suchcameras can be arranged to point upward, downward, or level with respectto the camera support and the shelves. This advantageously permits areduction in glare from products having highly reflective surfaces,since multiple cameras pointed in slightly different directions canresult in at least one image with little or no glare.

Electronic control unit 120 contains an autonomous robot sensing andnavigation control module 124 that manages robot responses. Robotposition localization may utilize external markers and fiducials, orrely solely on localization information provided by robot-mountedsensors. Sensors for position determination include previously notedimaging, optical, ultrasonic sonar, radar, Lidar, Time of Flight,structured light, or other means of measuring distance between the robotand the environment, or incremental distance traveled by the mobilebase, using techniques that include but are not limited totriangulation, visual flow, visual odometry and wheel odometry.

Electronic control unit 120 also provides image processing using acamera control and data processing module 122. Autonomous robot sensingand navigation control module 124 manages robot responses, andcommunication module 126 manages data input and output. The cameracontrol and data processing module 122 can include a separate datastorage module 123 (e.g. solid state hard drives) connected to aprocessing module 125. The communication module 126 is connected to theprocessing module 125 to transfer realogram data or panoramic images toremote locations, including store servers or other supported camerasystems, and additionally receive inventory information to aid inrealogram construction. In certain embodiments, realogram data isprimarily stored and images are processed within the autonomous robot.Advantageously, this reduces data transfer requirements, and permitsoperation even when local or cloud servers are not available.

FIG. 2 is a cartoon 200 illustrating two autonomous robots 230 and 232,similar to that discussed with respect to FIG. 1, inspecting oppositeshelves 202 in an aisle. As shown each robot follows path 205 along thelength of an aisle, with multiple cameras capturing images of theshelves 202.

In some embodiments, the robots 230 and 232 support at least one rangefinding sensor to measure distance between the multiple cameras and theshelves and products on shelves, with an accuracy of less than 5 cm, andwith a typical accuracy range between about 5 cm and 1 mm. As will beappreciated, LIDAR or other range sensing instruments with similaraccuracy can also be used in selected applications. Using absolutelocation sensors, relative distance measurements to the shelves,triangulation to a known landmark, conventional simultaneouslocalization and mapping (SLAM) methodologies, or relying on beaconspositioned at known locations in a blueprint or a previously built map,the robots 230 and 232 can move along a path generally parallel toshelves 202. As the robots move, vertically positioned cameras aresynchronized to simultaneously capture images of the shelves 202. Incertain embodiments, a depth map of the shelves and products is createdby measuring distances from the shelf cameras to the shelves andproducts over the length of the shelving unit using image depth sensorsand or laser ranging instrumentation. The depth map is registered ontothe images captured by the shelf cameras, so as the location of eachpixel on target can be estimated in 3D. Using available information,consecutive images can be stitched together to create panoramic imagesthat spans an entire shelving unit. The consecutive images can be firststitched vertically among all the cameras, and then horizontally andincrementally stitched with each new consecutive set of vertical imagesas the robots 230 and 232 move along an aisle.

FIG. 3 is an illustration of various systems and electronic modules 300supported by an autonomous robot having robot navigation and sensing310. Inventory cameras 340 are moved into a desired position with theaid of robot navigation and sensing module 310. Lights 350 are directedtoward product inventory and inventory camera control and imagereconstruction 312 takes a series of inventory photos (and optionaldepth measurements) that can be stitched together to help form or updatea realogram. Panoramic images, realogram data, or other inventoryrelated information is handled by an inventory data and local updatemodule 314, which can transmit or receive relevant information viacommunication system 316. Data can be communicated to a server local tothe store, or transmitted by suitable internet or networking devices toremote servers or cloud accessible data sites.

Inventory cameras 340 can include one or more movable cameras, zoomcameras, focusable cameras, wide-field cameras, infrared cameras,ultra-violet cameras, or other specialty cameras to aid in productidentification or image construction. For example, a wide-field cameracan be used to create an image organizing template into which data fromhigher resolution cameras with a narrow field of view are mapped orregistered. As another example, a tilt controllable, high resolutioncamera positioned on the camera support roughly at a height of a shelflip can be used to read shelf attached bar codes, identifying numbers,or labels. In certain embodiments, conventional RGB CMOS or CCD sensorscan be used, alone or in combination with spectral filters that mayinclude narrowband, wideband, or polarization filters. Embodiments canalso include sensors capable of detecting infrared, ultraviolet, orother wavelengths to allow for hyperspectral image processing. This canallow, for example, monitoring and tracking of markers, labels or guidesthat are not visible to people, or using flashing light in the invisiblespectrum that do not induce discomfort of health risk while reducingenergy consumption and motion blur.

Lights can be mounted along with, or separately from, the sensors, andcan include monochromatic or near monochromatic light sources such aslasers, light emitting diodes (LEDs), or organic light emitting diodes(OLEDs). Broadband light sources may be provided by multiple LEDs ofvarying wavelength (including infrared or ultraviolet LEDs), halogenlamps or other suitable conventional light source. Various spectralfilters that may include narrowband, wideband, or polarization filtersand light shields, lenses, mirrors, reflective surfaces, diffusers,concentrators, or other optics can provide wide light beams for areaillumination or tightly focused beams for improved local illuminationintensity.

According to some embodiments, both cameras 340 and lights 350 can bemovably mounted. For example, hinged, rail, electromagnetic piston, orother suitable actuating mechanisms used to programmatically rotate,elevate, depress, oscillate, or laterally or vertically repositioncameras or lights.

In still other embodiments, one or more of the cameras can be mounted insuch a way as to take advantage of the rolling shutter effects anddirection of travel of the autonomous robot. Aligning a camera in such away as to take advantage of the “rasterized” delay of the rollingshutter can reduce artifacts (elongation/shortening) that can occurwhile the robot is traveling in its path.

Inventory data 314 can include but is not limited to an inventorydatabase capable of storing data on a plurality of products, eachproduct associated with a product type, product dimensions, a product 3Dmodel, a product image and a current product price, shelf location,shelf inventory count and number of facings. Realograms captured andcreated at different times can be stored, and data analysis used toimprove estimates of product availability. In certain embodiments,frequency of realogram creation can be increased or reduced.

The communication system 316 can include connections to either a wiredor wireless connect subsystem for interaction with devices such asservers, desktop computers, laptops, tablets, or smart phones. Data andcontrol signals can be received, generated, or transported betweenvarieties of external data sources, including wireless networks,personal area networks, cellular networks, the Internet, or cloudmediated data sources. In addition, sources of local data (e.g. a harddrive, solid state drive, flash memory, or any other suitable memory,including dynamic memory, such as SRAM or DRAM) that can allow for localdata storage of user-specified preferences or protocols. In oneparticular embodiment, multiple communication systems can be provided.For example, a direct Wi-Fi connection (802.11b/g/n) can be used as wellas a separate 4G cellular connection.

Remote server 318 can include, but is not limited to servers, desktopcomputers, laptops, tablets, or smart phones. Remote server embodimentsmay also be implemented in cloud computing environments. Cloud computingmay be defined as a model for enabling ubiquitous, convenient, on-demandnetwork access to a shared pool of configurable computing resources(e.g., networks, servers, storage, applications, and services) that canbe rapidly provisioned via virtualization and released with minimalmanagement effort or service provider interaction, and then scaledaccordingly. A cloud model can be composed of various characteristics(e.g., on-demand self-service, broad network access, resource pooling,rapid elasticity, measured service, etc.), service models (e.g.,Software as a Service (“SaaS”), Platform as a Service (“PaaS”),Infrastructure as a Service (“IaaS”), and deployment models (e.g.,private cloud, community cloud, public cloud, hybrid cloud, etc.).

FIG. 4 is an illustration of realogram or panorama updating steps in oneembodiment of operation. As seen in flow chart 400, a robot moves to anidentified position and proceeds along an aisle path at a predetermineddistance (step 410). If the path is blocked by people or objects, therobot can wait till the path is unobstructed, begin movement and slowdown, wait as it nears the obstruction, move along the path untilrequired to divert around the object before reacquiring the path, orsimply select an alternative aisle.

In step 412, multiple images are captured and stitched together todefine an image panorama. Optionally, in certain embodiments a panoramicor widefield camera can capture a single large image. These images,along with optional depth information created by a laser ranging system,an infrared depth sensor, or similar system capable of distinguishingdepth at a decimeter or less scale, are used to create either panoramaor a realogram (step 414). This information is communicated to a cloudor remote server (step 416) to create, change, or update a panoramaand/or realogram with data derived from shelf labels, bar codes, andproduct identification databases to identify products. A realogram iscreated using panorama images and data, and can be used by, for example,store managers, stocking employees, or customer assistantrepresentatives to localize product and label placement, estimateproduct count, count the number of product facings, or even identify orlocate missing products. Additionally, in some embodiments, realogram orother information received from other robots, from updated productdatabases, or from other stores can be used to update or assist in thecreation of subsequent realograms (step 418).

FIGS. 5A and B are respectively examples in side view and cross sectionof an autonomous robot 500 capable of acting as a mobile base for acamera system in accordance with this disclosure. The robot navigationand sensing unit includes a top mount sensor module 510 with a number offorward, side, rear, and top mounted cameras. A vertically aligned arrayof lights 520 is sited next to a vertically arranged line of cameras530, and both are supported by a drive base 540 that includes controlelectronics, power, and docking interconnects. Mobility is provided bydrive wheels 560, and stability is improved by caster wheels 550.

Inventory monitoring can rely on use of autonomous robot camera systemimages. Typically, multiple images are processed, combined, andsegmented for further analysis. Segmented images can assist in defininga product bounding box that putatively identifies a product facing. Thisinformation is often necessary to develop a product library. A segmentedimage can include multiple product bounding boxes, typically rangingfrom dozens to hundreds of outlined or distinct image areas. Thebounding boxes can surround either product facings, groups of products,or gaps between products. Products within product bounding boxes can bemanually identified, identified using crowd source or paid reviewerimage identification systems, identified with or without the aid of aninitial planogram, or automatically identified using various imageclassifiers discussed herein. Gaps between products are useful foridentifying shelf spacings, product separation, or missing/absentinventory.

Automatic identification can be performed using an autonomous robot,alone or in combination with an external image classifier system. Incertain embodiments, a product bounding box can be defined as thehorizontal space on the shelf occupied by one or more copies (facings)of the same product, along with the vertical space spanning the distancebetween a current shelf and the shelf above it. When the current shelfis the top shelf, the vertical space is a number generally correspondingto the distance to top of the fixture. The vertical space canalternatively be top of the product as sensed by depth sensors.

Image segmentation to automatically assist in creation of productbounding boxes and product identification can rely on use of imagetemplates in some embodiments. Typically, each image template iscompared with the image captured by a camera system mounted on anautonomous robot. If a match is positive, the matched section of theimage is used as the image segmentation for that product

Segmentation can be improved by training classifiers on annotatedtraining data sets, where bounding boxes are manually drawn aroundproducts. Training can be performed with supervised or unsupervisedmachine learning, deep learning, or hybrid machine and deep learningtechniques, including but not limited to convolutional neural networks.

Some methods include reducing the number of image templates that must beconsidered by only matching the templates that correspond to the productidentifier; or to the product objects who are proximal to the shelflocation being scanned. Product objects can include but are not limitedto:

-   -   a product identifier

One or more sets of descriptors

Confidence levels for each set of descriptors

One or more shelf position metric estimates

Confidence levels for each shelf position metric estimate

One or more shelf position topological estimates

Count number for each shelf position topological estimate

One or more image templates of the product

Dimensions of the product

Product objects can be updated, manually or automatically revised,augmented, or corrected, and changed to match changing productspecifications.

Some methods further detect if a product is oriented differently thanthe externally sourced image. If a template match cannot be found, butthe product descriptor does find a high-likelihood match, this isindicative of a product orientation that is different from that of theimage sourced externally to the mobile base system. If the externallysourced image is known to be a front view of the product, then thismethod identifies products that are improperly oriented on the shelf.The angular deviation of the improperly oriented product can beestimated and an affine transformation between the set of descriptors ofthe externally sourced image and the segmented portion of the imagecomputed.

For situations where template matching is successful, the productsegmentation in the image can be considered accurate, and the realdimensions of the product are compared to the apparent dimensions of theproduct in the image to extract a distance estimate between the imagingsensor and the product. Additionally, the apparent position of theproduct in the image combined with the distance estimate enable thecomputation of the three-dimensional position and orientation betweenthe imaging sensor and the product.

Some methods extract topological shelf positions for each product fromthe planogram. These methods further increase the scope of the productobject identified through segmentation and redefine it as containing:

One product identifier

One or more sets of descriptors

Confidence levels for each set of descriptors

One or more shelf position metric estimates

Confidence levels for each shelf position metric estimate

One or more shelf position topological estimates

Count number for each shelf position topological estimate

One or more image templates of the product

Dimensions of the product

One or more topological shelf positions from planogram

In other embodiments, RFID tags, wireless beacons, locators, or trackerscan be used alone or in combination to assist in defining a productbounding box. For example, in some embodiments, an autonomous robot canbe additionally equipped with one or more RFID readers. Performing aninventory count of products equipped with RFID tags can proceed in oneembodiment as follows:

The total number of tags for each product is communicated by aninventory management software to an RFID reader mounted on or associatedwith an autonomous robot;

The RFID reader collects RFID tags while the autonomous robot is eitherstationary or moving;

If the RFID reader does not collect all the tags for a given product,and:

If the autonomous robot is moving, then the autonomous robot stops in anattempt to collect the remaining tags, or

If the autonomous robot is stopped, move the autonomous robot in apredetermined search path in an attempt to collect the remaining tags.

With suitable changes, Bluetooth, Near Field Communication, or otherconventional wireless system can be used in place of RFID systems.

In some embodiments, visual images based on numeric, alphabetic, one ortwo-dimensional bar codes, or similar image based shelf or productlabels can be used alone or in combination with various image featuresto segment images and assist in defining a product bounding box. Eachshelf image can be analyzed and segmented to detect individual pricetags or product labels on the shelf. Alternatively, or in addition, eachimage can be used to detect identifying indicia of individual productsand product packages. Segmentation can use techniques that include butare not limited to:

-   -   Edge detection;    -   Depth estimation using depth estimation techniques that include        but are not limited to:

Stereo camera

Structure from motion

Structure from focus

Depth camera using time of flight

Depth camera using triangulation

Planar or 3D laser/lidar scanner

Color segmentation;

Product features including but not limited to product shapes, colors,texts, and aspect ratios;

Product shapes identified and learned using machine learning techniquessuch as convolutional neural networks and deep learning

Association of individual product images to identifiers based on alocation heuristic. The heuristic may locate the identifier below aproduct image or in another proximal location.

The heuristic may be informed from the planogram by correlating theplanned location of labels to the measured location of labels, and thenumber of facings for each facing group with the measured distancebetween consecutive labels divided by the width of the productcorresponding to the left label. These correlations can be optimized,for example by using graph theory approaches, to generate a maximumlikelihood correspondence between a facing group and a label.

Association may also be informed by mapping the left most label on ashelf with the left most group of self-similar facings, the right mostlabel on the same shelf with the right most label facing group, andworking inward until every facing group has an associated label.

Further, association may be informed by classifiers trained onhand-annotated associations from training data sets and using heuristicssimilar to the ones described above. Some methods further includeinferring the position of each identifier on the shelf by comparing thelocation of the identifier to the location of the beginning or end ofthe shelf on which the identifier is placed. Alternatively, methods canbe based on inferring the position of each product on the shelf bycomparing the location of the product to the location of the beginningor end of the shelf on which the product is placed.

In some methods, an identifier's shelf location and/or product locationare expressed in metric terms, i.e. measured distance from the beginningor end of a specific shelf. In other methods, an identifier's shelflocation and/or product location are expressed topologically, e.g. as asequence of identifiers from the start or the end of a specific shelf,and from the bottom of a shelf to the top, or from top to bottom. Forexample, a specific identifier may be third from the start of the fourthshelf.

If a product library is created or made available, the library can besearched for products objects with a large number of similar features toassist in developing a product bounding box. For each potential productobject match, the geometric consistency of the feature locations in thelibrary can be compared with the features in a shelf image. Some methodsfurther include indexing the sets of descriptor within the library forimproved searching performance and/or reduced storage requirements.Indexing methods include but are not limited to: hashing techniques,tree representations, and bag-of-words encodings. Alternatively,planogram information or product location information from the productlibrary can be used to reduce the number of products that must besearched to just those products contained within the imaged shelf. Instill other variations, identified products can be verified bysegmenting and decoding the price tag or product label locatedproximally to each identified product and comparing it to the productobject identifier.

FIG. 6 is a flowchart 600 illustrating in more detail one example of howa product bounding box such as previously discussed can be created frominformation captured by sensor and camera system supported by anautonomous robot. Turning to FIG. 6, in a first step 610, shelf labelsare detected either in individual shelf images, or in a stitchedpanorama. Classification algorithms such as convolution neural networksor other deep learning methods, template matching or HAAR cascades canbe used to aid in detection of each shelf label. Each shelf label isanalyzed to obtain one or more product identifiers. Analysis may includebut is not limited to optical character recognition, bar code scanning,QR code scanning, AR code scanning, or hologram code scanning. Productidentifiers may be UPC code, the product name, or a coded collection ofletters, numbers, or other symbols. If more than one identifier isavailable, a preferred identifier such as the UPC code can be selected.In certain embodiments, infrared or ultraviolet detectable productidentifiers embedded on product packaging or shelf labels can be used,as well as any other suitable tag, marker, or detectable identifyingindicia such as a visible UPC code or serial number on the productpackaging.

In optional step 612, an image location of the shelf label is registeredor compared to a depth map to recover its 3D location in space. Thedepth map can be created by use of one or more depth sensors that inferdepth from stereo images, project an infrared mesh overlay that allowsrough determination of object distance in an image, infer depth from thetime of flight of scanning lasers or LEDs reflecting off the target, orany other suitable method for building a depth map typically havingsub-millimeter to sub-centimeter resolution.

In step 614 a bounding box is defined as a perimeter enclosing one ormultiple facings of the same product, or any space on the shelfincluding but not limited a gap between products. The bounding box canbe manually defined, or automatically defined using trainingclassifiers, deep learning, image segmentation, or any other suitabletechnique or combination of techniques. A bounding box can be createdwith reference to labels grouped by height, with a horizontal distancebetween adjacent labels used to define the width of the bounding box forsingle facing products. For multiple facing products, the bounding boxwidth is subdivided in sections equal to the product width.

The height of the bounding box can be derived from the detection ofshelf heights. Shelf heights can be detected by analyzing the depth mapto identify horizontal indentations corresponding to the distancebetween the bottom of a shelf and products stacked below it. Thesehorizontal indentations correspond to shelf lips and measure shelfheight.

Alternatively, label groups can be filtered by horizontal span (definedas the horizontal distance between the first and last label of thegroup) according the following criteria:

A label group passes the filter if its horizontal span overlaps with thecurrent label group span.

A label group passes the filter if its horizontal span is distant fromthe current label group span by no more than a number representing a baywidth. Generally, bay widths are standard three or four-foot-wideshelves used throughout a store.

Order filtered label groups by height and select the label group that isnext highest after the current label group.

Alternatively, shelf heights can also be detected by deep learningclassifiers trained on manually annotated color and depth images (ifavailable) of the shelves.

The height of the bounding box can be fully defined once the heightdifference between the selected label group and the current label groupis determined.

For vertically stacked products, the height of the bounding box issubdivided in sections equal to the height of the product.

In certain embodiments, the previous method for determining the boundingbox can be probabilistically combined through estimating a confidencemeasure for each method and adding their results.

Next, in step 616, each bounding box is consistently associated with anidentifier based on a label location heuristic. The identifier can beselected to originate from either the left or the right shelf label. Theassociation of bounding boxes and identifier can further be refinedthrough optimization across an entire shelf section or aisle. Thebounding box, with identifiers, can be registered to a simple orpanoramic stitched image of the shelf, and image descriptors extractedfor the portion of the image contained in the bounding box. Methods forgenerating image descriptors include but are not limited to: imagetemplates, Histogram of Gradients, Histogram of Colors, the ScaleInvariant Feature Transform, Binary Robust Independent ElementaryFeatures, Maximally Stable Extremal Regions, Binary Robust InvariantScalable Keypoints, Fast Retina Keypoints, Kaze features, and variationsthereof.

An alternative to extracting product descriptors is to use the boundingboxes as labeled categories and train classifiers on the imagescontained in the bounding boxes. Classifiers may include those based ondeep structured learning, hierarchical learning, deep machine learning,or other suitable deep learning algorithms associated withconvolutional, feedforward, recurrent, or other suitable neural network.A deep learning based classifier can automatically learn imagedescriptors based on an annotated training data. For example, deeplearning based image descriptors can be hierarchical, corresponding tomultiple layers in deep convolutional neural networks. The final layerof a convolutional layer network outputs the confidence values of theproduct being in one of the designated image categories. The imagedescriptor generator part and the classification part get integrated ina convolutional neural network and these two parts are trained togetherusing a training set.

Alternatively, or in addition, embodiments that use both deep learningbased image descriptors and conventional image descriptors can becombined in a hybrid system.

In step 618, the image descriptors can be classified and labelled withthe identifier. Classification algorithms that can include but are notlimited to support vector machine. This process can be repeated forevery image of the bounding box associated to the same identifier,whether the image is captured in the same store at different times, orin different stores. In time, this allows automatically building aproduct library (i.e. the “Library of Products”), without requiring aninitial planogram or storage of specific product databases.

For those embodiments utilizing deep learning based image descriptors,the neural network classifier can be part of the same trainedconvolutional neural network. The automatically learned features, whichare extracted from different layers of the convolutional neural network,can be used in a larger product recognition system. These features canbe used in conjunction with other classifiers or with conventional imagedescriptors in a hybrid system.

FIG. 7 is a flow chart 700 illustrating additional process details formanaging one embodiment of a library of products. The library ofproducts can be stored in a single autonomous robot such as describedherein, distributed between two or more autonomous robots, or stored ina local, remote, or cloud server, in whole or in part. In oneembodiment, step 710 requires extraction of one set of descriptors fromeach product image associated to an identifier. One or more sets ofdescriptors can be developed, confidence levels for each set ofdescriptors set; dates at which each set of descriptors was generatedrecorded; one or more shelf position metric estimates made; confidencelevels for each shelf position metric estimated; one or more shelfposition topological estimated; or a count number for each shelfposition topological estimated. In other embodiments, one or more imagetemplates of the product or dimensions of the product can be used todetermine product object scope.

In step 714, each identifier and set of product descriptors is stored ina database or library of product objects as follows:

If the identifier does not match an existing identifier in the library,create a new object containing the identifier, the set of productdescriptors, an entry confidence level for the set of productdescriptors, and the date. The entry confidence level is unique.

If the product's identifier matches an existing identifier in thelibrary, compute a score for each existing set of descriptors thatdescribes the likelihood that the new and existing sets of descriptorswere extracted from the same object.

If the highest likelihood score exceeds a heuristic value for a positivematch: combine the new set of descriptors with the highest likelihoodfeature set; and increase the confidence level of this set ofdescriptors relative to the other sets of descriptors in the object; andappend the date to that of the existing set of descriptors.

If the highest likelihood score does not exceed a heuristic value for apositive match: add the set of descriptors to the object; set theconfidence to the entry level; and add the date.

For those embodiments utilizing deep learning based image recognition,the input image is classified belonging to one of the product categoriesusing convolutional neural network which outputs the confidence level.This confidence level is then used to store the image descriptors in thelibrary in the same fashion as above.

In step 716, the descriptor sets within the library can be used forimproved searching performance and/or reduced storage requirements.Indexing methods include but are not limited to: hashing techniques,tree representations, and bag-of-words encodings.

In step 718, the product library can optionally be pruned to reduceerrors and database size. Pruning of sets of descriptors and metric ortopological shelf positions can occur as follows:

Sets of descriptors: delete all sets whose confidence level is below aheuristic value, along with their confidence level and date entries,except if they originate from images sourced externally to the mobilebase system.

Metric shelf positions: delete all metric positions whose confidencelevel is below a heuristic value except if their date of entry is thelast date on record, along with their confidence level and date entries

Topological shelf positions: delete all topological positions whosecount number is below a heuristic value except if their date of entry isthe last date on record, along with their count number and date entries

Alternatively, pruning can involve:

iv) Sets of descriptors: delete all sets except for the set with thehighest confidence level, along with their confidence level and dateentries, except if they originate from images sourced externally to themobile base system.

v) Metric shelf positions: delete all metric positions along with theirconfidence level and date entries, except the latest date, if itsassociated confidence level is higher than a heuristic value. Otherwise,keep the data from last two dates or more, until a confidence levelabove the heuristic value is found.

vi) Topological shelf positions: delete all topological positions alongwith their counter number and date entries, except the latest date, ifits associated count number is higher than a heuristic value. Otherwise,keep the data from last two dates or more, until a count number abovethe heuristic value is found.

In some embodiments, modifying the product library can utilize externalimage data sources to aid in product identification. These images can beacquired from one or more of the following sources:

Retailer carrying the product, where the image corresponds to theproduct identifier

Manufacturer of the product, after matching the product identifier withthe manufacturer's nomenclature and image

Third-party photography of the product

Online databases, image search engines, or data from online retailers ofproduct listings, or other Internet databases. Searches are conductedpreferably using the manufacturer's nomenclature that corresponds to theproduct identifier. In some methods, this search is performedautomatically and the one or more images inserted without humanintervention.

The one or more externally derived images can include at least one frontview of the product, and optionally additional views, such as back,sides, top and bottom, or different packaging appearances of the productthat correspond to the same identifier. If a product is orienteddifferently than the externally sourced image, the product descriptorcan attempt to find a high-likelihood match. In some embodiments, theangular deviation between a likely product and a differently orientedproduct can be determined by computing an affine transformation betweenthe set of descriptors of the externally sourced image and the availableproduct image.

For situations where template matching is successful, and the productsegmentation in the image is accurate, the real dimensions of theproduct can be compared to the apparent dimensions of the product in theimage to extract a distance estimate between the imaging sensor and theproduct. Additionally, the apparent position of the product in the imagecombined with the distance estimate enable the computation of thethree-dimensional position and orientation between the imaging sensorand the product.

In the deep learning based alternative embodiments, the productsegmentation can be performed by a convolutional neural network whichclassifies pixels as belonging to product interior and productbackground. The product dimensions are inferred from the segmentation.

Many modifications and other embodiments of the invention will come tothe mind of one skilled in the art having the benefit of the teachingspresented in the foregoing descriptions and the associated drawings.Therefore, it is understood that the invention is not to be limited tothe specific embodiments disclosed, and that modifications andembodiments are intended to be included within the scope of the appendedclaims. It is also understood that other embodiments of this inventionmay be practiced in the absence of an element/step not specificallydisclosed herein.

1. A inventory monitoring method, comprising the steps of: detecting andreading shelf labels on at least one shelf; defining one or morebounding boxes around potential inventory in an image taken with atleast one camera; and associating the bounding boxes in the image toshelf labels to build at least one of a product classifier or a productlibrary.
 2. The inventory monitoring method of claim 1, furthercomprising the step of allowing an autonomous robot to move beside theat least one shelf, with the autonomous robot acting as a movable basecapable of autonomously reading shelf labels and defining multiplebounding boxes.
 3. The inventory monitoring method of claim 1, furthercomprising the step of allowing an autonomous robot to move beside theat least one shelf, with the autonomous robot acting as a movable basehaving multiple cameras and a LIDAR range sensing system.
 4. Theinventory monitoring method of claim 1, further comprising the step ofallowing an autonomous robot to move beside the at least one shelf, withthe autonomous robot acting as a movable base to capture a depth map ofshelves and products positioned on shelves.
 5. The inventory monitoringmethod of claim 1, further comprising the step of building a productlibrary with a product scope containing at least one or more of thefollowing: one product identifier; one or more sets of descriptors;confidence levels for each set of descriptors; dates at which each setof descriptors was generated; one or more shelf position metricestimates; confidence levels for each shelf position metric estimate;one or more shelf position topological estimates; a count number foreach shelf position topological estimate; one or more image templates ofthe product; and dimensions of the product.
 6. The inventory monitoringmethod of claim 1, further comprising the step of using manual input toidentify potential inventory designated in the multiple bounding boxesin the image taken with at least one camera
 7. The inventory monitoringmethod of claim 1, wherein bounding boxes around both potentialinventory and gaps between potential inventory are automaticallydefined.
 8. The inventory monitoring method of claim 1, wherein boundingboxes are manually associated with at least one of a shelf label or anon-product marker.
 9. The inventory monitoring method of claim 1,wherein bounding boxes are automatically associated with at least one ofa shelf label or an on-product marker.
 10. The inventory monitoringmethod of claim 1, further comprising the step of building the productlibrary without use of an initial planogram.
 11. A system for building aproduct classifier, comprising: an image capture unit; a shelf labeldetector and reader to capture shelf label data; a processing module todefine multiple bounding boxes, and a trainable image classifier toassociate images within the bounding boxes with the shelf label data,the images being provided by the image capture unit.
 12. The system ofclaim 11, wherein the image capture unit is mounted on an autonomousrobot and is connected to the shelf label detector and reader.
 13. Thesystem of claim 11, wherein the trainable image classifier is trainedusing machine learning.
 14. The system of claim 11, wherein thetrainable image classifier is trained using a deep learning system. 15.A inventory monitoring method, comprising the steps of: detecting andreading shelf labels; detecting distance from at least one camera to atleast one shelf; defining bounding boxes around potential inventory inan image taken with at least one camera; and associating selectedbounding boxes in the image to selected shelf labels to build at leastone of a product classifier or a product library.
 16. The inventorymonitoring method of claim 15, further comprising the step of allowingan autonomous robot to move beside the at least one shelf, with theautonomous robot acting as a movable base capable of detecting distance,reading shelf labels, and defining bounding boxes.
 17. The inventorymonitoring method of claim 15, further comprising the step of allowingan autonomous robot to move beside the at least one shelf, with theautonomous robot acting as a movable base having multiple cameras and atleast one of a LIDAR range sensing system or a depth camera.
 18. Theinventory monitoring method of claim 15, further comprising the step ofallowing an autonomous robot to move beside the at least one shelf, withthe autonomous robot acting as a movable base to capture a depth map ofshelves and of products positioned on shelves.
 19. The inventorymonitoring method of claim 15, further comprising the step of building aproduct library with a product scope containing at least one or more ofthe following: one product identifier; one or more sets of descriptors;confidence levels for each set of descriptors; dates at which each setof descriptors was generated; one or more shelf position metricestimates; confidence levels for each shelf position metric estimate;one or more shelf position topological estimates; a count number foreach shelf position topological estimate; one or more image templates ofthe product; and dimensions of the product.
 20. The inventory monitoringmethod of claim 15, further comprising the step of building a productlibrary using manual input to identify potential inventory designated inthe multiple bounding boxes in the image taken with at least one camera21. The inventory monitoring method of claim 15, wherein the boundingboxes can surround gaps between potential inventory.
 22. The inventorymonitoring method of claim 15, further comprising the step of buildingthe product library without use of an initial planogram.